Personalized glucose prediction using in situ data only

Rohan Singh^*Marouane ToumiMarcel Salathé^*

• Swiss Federal Institute of Technology Lausanne, Lausanne, Switzerland

The worldwide rise in blood glucose levels is a major health concern, as various metabolic diseases become increasingly common. Diet, a modifiable health behavior, is a primary target for the preventive management of glucose levels. Recent studies have shown that blood glucose responses after meals (post-prandial glucose responses, PPGR) can vary greatly among individuals, even with identical food consumption, and demonstrated accurate PPGR prediction using various features like microbiome data and blood parameters. Our study addresses whether accurate PPGR prediction can be achieved with a limited and easily obtainable set of data collected in real-world, everyday settings. Here, we show that a machine learning algorithm with such realworld data (RWD) collected from a digital cohort with over 1,000 participants can achieve high accuracy in PPGR prediction. Interestingly, we find that the best PPGR prediction model only required glycemic and temporally resolved diet data. This ability to predict PPGR accurately without the need for biological lab analysis offers a path towards highly scalable personalized nutrition and glucose management strategies.